Experience
Research and engineering roles centered on ML infrastructure, evaluation, and robotics pipelines.

Agentic AI Engineer
Sunrise (Zurich, Switzerland)- Designing and building agentic AI solutions on Google Cloud Platform, leveraging Vertex AI for model orchestration and deployment.
- Developing MCP (Model Context Protocol) servers to connect agents with internal tools, data sources, and enterprise systems.
- Engineering large-scale data processing pipelines that power agentic workflows in production.

External Research Collaborator (AI for Robotics)
Intelligent Sensing Lab (ISLab), Univ. of Milano-Bicocca- Research project: “Data Collection and Analysis of Human–Machine Interaction in the Context of Industry 5.0” (Cod. 25CE114).
- Part of HOMEY – A Human-centric IoE-based Framework for Supporting the Transition Towards Industry 5.0 (PRIN 2022, ID 2022-NAZ-0329/PER, funded by the EU – NextGenerationEU).
- Collected and analyzed human-robot interaction data using the LeRobot framework.
- Extended ACT policy models with visual task encoding, evaluated multi-task learning performance, and benchmarked wearable-sensor-based control (Movella Xsens Dot) vs. leader-arm teleoperation.

Data Science Intern - Generative AI
KNIME GmbH (Berlin, Germany)- Worked within the Evangelism Team, focusing on the integration of Generative AI and data analytics through KNIME workflows.
- Developed a complete data app assembling the KPI dashboard featured in KNIME’s official blog post “6 CEO KPIs and How to Measure Them,” including workflows for both the KPI data warehouse and the dynamic dashboard.
- Authored the blog “Visualize Your Revenue Growth KPI with K-AI.”
- Built GenAI workflows using multimodal local LLMs to automatically explain KPI reports, and created the corresponding workflow, presentation slides, and blog post. This project was presented at 5+ conferences.
- Designed feature engineering pipelines powered by Generative AI, complemented by technical blogs and documentation.
- Co-facilitated the KNIME GenAI Learnathon at the University of Pisa, mentoring over 100 students.

Research Intern - Microelectronics
Microelectronics Group - UniMiB (Milan, Italy)- Worked on a research project in microelectronics, focused on Protond Sound Detectors.
- The experiment is based on sensing the weak thermoacoustic signal emitted by the fast energy deposition (and/or theheating process) at the end of the beam range (Bragg Peak)
- Implemented a Matlab simulation of a chopper-stabilized preamplifier to model and reduce low-frequency noise in the analog frontend.
- Analyzed the performance of the preamplifier in terms of noise reduction and signal fidelity.
Education
Academic background grounding applied ML engineering with physics foundations.
MSc in Data Science
University of Milano-Bicocca
Graduate coursework focused on ML systems, data engineering, and applied AI.
BSc in Physics
University of Milano-Bicocca
Focused on microelectronics and completed a thesis on chopper preamplifier modeling.
Licenses & Certifications
Professional certifications and achievements in data science, AI, and analytics.

Claude 101
Anthropic

AI Agent Fundamentals
Databricks

AI Agents Fundamentals
Hugging Face
Credential ID sergiov2000

International Student
Università degli Studi di Milano-Bicocca

Proficiency in KNIME Software for Collaboration and Productionization
KNIME

Advanced Proficiency in KNIME Analytics Platform
KNIME

Basic Proficiency in KNIME Analytics Platform
KNIME

English C1 BBetween
Universita degli Studi di Milano-Bicocca
Credential ID m9VSIr-7Q3qoNGPXuKUJbw

1st place in the "Machine Learning Challenge" - Spring 2024
KNIME
Startup Collaborations
Selective early-stage product work at the intersection of computer vision and practical user tools.

R&D Specialist
QuickWine- Contributed to QuickWine, a startup project recognized within the LEARN program of Start for Future.
- Developed the recommendation engine and generated simulated supermarket datasets in a private GitHub repository.

Technical Co-Founder
Calisthenics Assistant
- Co-founded an early-stage fitness-tech project focused on video-based analysis of calisthenics technique.
- Built Python computer-vision tools to evaluate handstands, count push-ups, and overlay form feedback directly on videos.
- Expanded the system beyond the initial prototype with support for additional bodyweight exercises and coaching logic.